matrixStats.benchmarks

---

colAnys() and rowAnys() benchmarks

This report benchmark the performance of colAnys() and rowAnys() against alternative methods.

Alternative methods

• apply() + any()
• colSums() > 0 or rowSums() > 0

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = "logical")

Results

10x10 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5179746 276.7    8529671 455.6  8529671 455.6
Vcells 9512341  72.6   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5171145 276.2    8529671 455.6  8529671 455.6
Vcells 9484279  72.4   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	0.003369	0.0037380	0.0045488	0.0046250	0.0048980	0.019739
3	colSums > 0	0.006011	0.0063950	0.0076344	0.0072150	0.0077430	0.036104
2	apply+any	0.033466	0.0343405	0.0360280	0.0350695	0.0358385	0.081195

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums > 0	1.784209	1.710808	1.678334	1.560000	1.580849	1.829069
2	apply+any	9.933511	9.186865	7.920285	7.582595	7.316966	4.113430

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAnys	0.003514	0.0038390	0.0047763	0.0049670	0.0052155	0.018834
3	rowSums > 0	0.006739	0.0070855	0.0079660	0.0076285	0.0082740	0.029251
2	apply+any	0.033386	0.0343190	0.0356151	0.0348535	0.0355940	0.081946

	expr	min	lq	mean	median	uq	max
1	rowAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums > 0	1.917758	1.845663	1.667837	1.535837	1.586425	1.553095
2	apply+any	9.500854	8.939568	7.456670	7.017012	6.824657	4.350961

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x10 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	3.369	3.738	4.54882	4.625	4.8980	19.739
2	rowAnys	3.514	3.839	4.77627	4.967	5.2155	18.834

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000000
2	rowAnys	1.043039	1.02702	1.050002	1.073946	1.064822	0.9541517

Figure: Benchmarking of colAnys() and rowAnys() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5169723 276.1    8529671 455.6  8529671 455.6
Vcells 9290506  70.9   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5169699 276.1    8529671 455.6  8529671 455.6
Vcells 9295519  71.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	0.003781	0.0046570	0.0058371	0.0055565	0.0061375	0.027023
3	colSums > 0	0.015332	0.0170265	0.0188621	0.0180650	0.0200460	0.041649
2	apply+any	0.198164	0.2068690	0.2289510	0.2231090	0.2528385	0.328815

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums > 0	4.055012	3.656109	3.231403	3.251147	3.266151	1.541243
2	apply+any	52.410473	44.421087	39.223278	40.152794	41.195682	12.167968

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAnys	0.010892	0.012502	0.0140718	0.0134200	0.0150110	0.032720
3	rowSums > 0	0.044789	0.047577	0.0527260	0.0512480	0.0580985	0.082739
2	apply+any	0.188202	0.196207	0.2221017	0.2157725	0.2457590	0.327130

	expr	min	lq	mean	median	uq	max
1	rowAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums > 0	4.112101	3.805551	3.746918	3.818778	3.870395	2.528698
2	apply+any	17.278920	15.694049	15.783439	16.078428	16.371927	9.997861

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x100 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	3.781	4.657	5.83712	5.5565	6.1375	27.023
2	rowAnys	10.892	12.502	14.07182	13.4200	15.0110	32.720

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowAnys	2.880719	2.684561	2.410747	2.415189	2.445784	1.21082

Figure: Benchmarking of colAnys() and rowAnys() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5170455 276.2    8529671 455.6  8529671 455.6
Vcells 9294000  71.0   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5170449 276.2    8529671 455.6  8529671 455.6
Vcells 9299043  71.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	0.003171	0.0038475	0.0048026	0.0045420	0.0050840	0.018961
3	colSums > 0	0.017068	0.0187995	0.0199878	0.0196230	0.0207900	0.035880
2	apply+any	0.110909	0.1163095	0.1237920	0.1261835	0.1293025	0.192247

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.00000	1.000000	1.000000	1.00000	1.000000
3	colSums > 0	5.382529	4.88616	4.161841	4.320343	4.08930	1.892305
2	apply+any	34.976033	30.22989	25.775869	27.781484	25.43322	10.139075

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAnys	0.011827	0.0140215	0.0160236	0.0152930	0.0170775	0.030842
2	apply+any	0.097301	0.1010985	0.1194628	0.1143040	0.1299365	0.195430
3	rowSums > 0	0.167845	0.1742380	0.1923458	0.1928465	0.2093100	0.230652

	expr	min	lq	mean	median	uq	max
1	rowAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	apply+any	8.227023	7.210248	7.455427	7.474269	7.608637	6.336489
3	rowSums > 0	14.191680	12.426488	12.003899	12.610116	12.256478	7.478503

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x10 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	3.171	3.8475	4.80263	4.542	5.0840	18.961
2	rowAnys	11.827	14.0215	16.02361	15.293	17.0775	30.842

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys	3.729738	3.644315	3.336424	3.367019	3.359068	1.626602

Figure: Benchmarking of colAnys() and rowAnys() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5170687 276.2    8529671 455.6  8529671 455.6
Vcells 9294767  71.0   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5170663 276.2    8529671 455.6  8529671 455.6
Vcells 9299780  71.0   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	colSums > 0	0.013500	0.0148425	0.0169421	0.0157770	0.0177990	0.038084
1	colAnys	0.015037	0.0169750	0.0196664	0.0184330	0.0205435	0.043810
2	apply+any	0.863858	0.9135615	1.0252330	0.9707885	1.0985930	1.429867

	expr	min	lq	mean	median	uq	max
3	colSums > 0	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
1	colAnys	1.113852	1.143675	1.16080	1.168346	1.154194	1.150352
2	apply+any	63.989481	61.550379	60.51389	61.531882	61.722175	37.545085

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAnys	0.025629	0.0292995	0.0344592	0.031665	0.038367	0.073215
3	rowSums > 0	0.028577	0.0308205	0.0357424	0.032762	0.038074	0.067021
2	apply+any	0.870325	0.9096820	1.0249298	0.947311	1.108584	1.530552

	expr	min	lq	mean	median	uq	max
1	rowAnys	1.000000	1.000000	1.000000	1.000000	1.0000000	1.0000000
3	rowSums > 0	1.115026	1.051912	1.037236	1.034644	0.9923632	0.9153998
2	apply+any	33.958602	31.047697	29.743259	29.916659	28.8942060	20.9048965

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 10x1000 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	15.037	16.9750	19.66641	18.433	20.5435	43.810
2	rowAnys	25.629	29.2995	34.45923	31.665	38.3670	73.215

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys	1.704396	1.726038	1.752187	1.717843	1.867598	1.671194

Figure: Benchmarking of colAnys() and rowAnys() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5170877 276.2    8529671 455.6  8529671 455.6
Vcells 9295254  71.0   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5170865 276.2    8529671 455.6  8529671 455.6
Vcells 9345287  71.3   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	0.016351	0.0184375	0.0205998	0.0196440	0.021219	0.044673
3	colSums > 0	0.081160	0.0879225	0.0936510	0.0907155	0.094624	0.143238
2	apply+any	1.371886	1.4840435	1.5837545	1.5252055	1.567319	2.514333

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
3	colSums > 0	4.963611	4.768678	4.546215	4.617975	4.45940	3.206366
2	apply+any	83.902269	80.490495	76.882146	77.642308	73.86394	56.283057

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAnys	0.072548	0.0807825	0.0865152	0.082799	0.087472	0.152590
3	rowSums > 0	0.230758	0.2500765	0.2709630	0.253867	0.277809	0.395284
2	apply+any	1.374199	1.4937530	1.5805760	1.531343	1.588420	2.482158

	expr	min	lq	mean	median	uq	max
1	rowAnys	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
3	rowSums > 0	3.180763	3.095677	3.13197	3.066064	3.175976	2.590497
2	apply+any	18.941928	18.491047	18.26934	18.494704	18.159177	16.266846

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 100x1000 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	16.351	18.4375	20.59977	19.644	21.219	44.673
2	rowAnys	72.548	80.7825	86.51521	82.799	87.472	152.590

	expr	min	lq	mean	median	uq	max
1	colAnys	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowAnys	4.436915	4.381424	4.199814	4.214977	4.122343	3.41571

Figure: Benchmarking of colAnys() and rowAnys() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5171083 276.2    8529671 455.6  8529671 455.6
Vcells 9295919  71.0   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAnys = colAnys(X), `apply+any` = apply(X, MARGIN = 2L, FUN = any), 
+     `colSums > 0` = (colSums(X) > 0L), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5171071 276.2    8529671 455.6  8529671 455.6
Vcells 9345952  71.4   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAnys = rowAnys(X), `apply+any` = apply(X, MARGIN = 1L, FUN = any), 
+     `rowSums > 0` = (rowSums(X) > 0L), unit = "ms")

Table: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	0.003413	0.0045435	0.0060280	0.0054805	0.0067385	0.030601
3	colSums > 0	0.082798	0.0877285	0.1015405	0.0931570	0.1120050	0.165285
2	apply+any	0.634206	0.6663190	0.7640796	0.7152840	0.8382390	1.114133

	expr	min	lq	mean	median	uq	max
1	colAnys	1.0000	1.00000	1.00000	1.0000	1.00000	1.000000
3	colSums > 0	24.2596	19.30857	16.84487	16.9979	16.62165	5.401294
2	apply+any	185.8207	146.65324	126.75549	130.5144	124.39549	36.408385

Table: Benchmarking of rowAnys(), apply+any() and rowSums > 0() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowAnys	0.060689	0.0652140	0.0730753	0.0679255	0.0793035	0.116305
3	rowSums > 0	0.324218	0.3425475	0.3810645	0.3611855	0.4070895	0.537751
2	apply+any	0.641484	0.6636305	0.7398351	0.6933770	0.7514955	1.161896

	expr	min	lq	mean	median	uq	max
1	rowAnys	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
3	rowSums > 0	5.342286	5.252668	5.21468	5.317377	5.133311	4.623627
2	apply+any	10.570021	10.176197	10.12428	10.207904	9.476196	9.990078

Figure: Benchmarking of colAnys(), apply+any() and colSums > 0() on 1000x100 data as well as rowAnys(), apply+any() and rowSums > 0() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys() and rowAnys() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colAnys	3.413	4.5435	6.02798	5.4805	6.7385	30.601
2	rowAnys	60.689	65.2140	73.07533	67.9255	79.3035	116.305

	expr	min	lq	mean	median	uq	max
1	colAnys	1.00000	1.00000	1.00000	1.00000	1.00000	1.000000
2	rowAnys	17.78172	14.35325	12.12269	12.39403	11.76872	3.800693

Figure: Benchmarking of colAnys() and rowAnys() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.1     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rappdirs_0.3.3          startup_0.15.0          labeling_0.4.2         
[67] bitops_1.0-7            base64enc_0.1-3         boot_1.3-28            
[70] gtable_0.3.0            DBI_1.1.1               markdown_1.1           
[73] R6_2.5.1                lpSolveAPI_5.5.2.0-17.7 rle_0.9.2              
[76] dplyr_1.0.7             fastmap_1.1.0           bit_4.0.4              
[79] utf8_1.2.2              parallel_4.1.1          Rcpp_1.0.7             
[82] vctrs_0.3.8             png_0.1-7               DEoptimR_1.0-9         
[85] tidyselect_1.1.1        xfun_0.25               coda_0.19-4            

Total processing time was 11.92 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colAnys')

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 18:51:03 (+0200 UTC). Powered by RSP.